Field of the Invention
This invention relates in general to the field of energy management, and more particularly to a demand response prediction system that employs fine-grained energy consumption baseline data.
Description of the Related Art
One problem with resources such as electricity, water, fossil fuels, and their derivatives (e.g., natural gas) is related to supply and demand. That is, production of a resource often is not in synchronization with demand for the resource. In addition, the delivery and transport infrastructure for these resources is limited in that it cannot instantaneously match production levels to provide for constantly fluctuating consumption levels. As anyone who has participated in a rolling blackout will concur, the times are more and more frequent when resource consumers are forced to face the realities of limited resource production.
Another problem with resources such as water and fossil fuels (which are ubiquitously employed to produce electricity) is their limited supply along with the detrimental impacts (e.g., carbon emissions) of their use. Public and political pressure for conservation of resources is prevalent, and the effects of this pressure is experienced across the spectrum of resource providers, resource producers and managers, and resource consumers.
It is no surprise, then, that the electrical power generation and distribution community has been taking proactive measures to protect limited instantaneous supplies of electrical power by 1) imposing demand charges on consumers in addition to their monthly usage charge and 2) providing incentives for conservation in the form of rebates and reduced charges. In prior years, consumers merely paid for the total amount of power that they consumed over a billing period. Today, most energy suppliers are not only charging customers for the total amount of electricity they have consumed over the billing period, but they are additionally charging for peak demand. Peak demand is the greatest amount of energy that a customer uses during a measured period of time, typically on the order of minutes. In addition, energy suppliers are providing rebate and incentive programs that reward consumers for so called energy efficiency upgrades (e.g., lighting and surrounding environment controlled by occupancy sensors, efficient cooling and refrigeration, etc.) in their facilities that result in reductions of both peak and overall demand. Similar programs are prevalent in the water production and consumption community as well.
Demand reduction and energy efficiency programs may be implemented and administered directly by energy providers (i.e., the utilities themselves) or they may be contracted out to third parties, so called energy services companies (ESCOs). ESCOs directly contract with energy consumers and also contract with the energy providers to, say, reduce the demand of a certain resource in a certain area by a specified percentage, where the reduction may be constrained to a certain period of time (i.e., via a demand response program) or the reduction may permanent (i.e., via an energy efficiency program).
The above examples are merely examples of the types of programs that are employed in the art to reduce consumption and foster conservation of limited resources. Regardless of the vehicle that is employed, what is important to both producers and consumers is that they be able to understand and appreciate the effects of demand reduction and efficiency actions that are performed, say, on individual buildings. How does a building manager know that the capital outlay made to replace 400 windows will result in savings that allow for return of capital within three years? How does an ESCO validate for a contracting regional transmission operator (e.g., Tennessee Valley Authority) that energy efficiency programs implemented on 1,000 consumers will result in a 15 percent reduction in baseline power consumption?
The answers to the above questions are not straightforward, primarily because, as one skilled in the art will appreciate, weather drives consumption. Weather is not the only driver in consumption, but it is significant. For instance, how can a building's energy consumption in January of one year be compared to its consumption in January of another year when average temperatures in the two month's being compared differ by 25 degrees? Is the difference between the two month's power consumption due to weather, or implementation of an energy efficiency program, or a combination of both?
Fortunately, those in the art have developed complex, but widely accepted, normalization techniques that provide for weather normalization of energy use data so that consumption by a building in two different months can be compared without the confusion associated with how outside temperature affects energy use. These modeling techniques provide for normalization of energy use data for buildings and groups of buildings, and they are accurate for the above purposes when employed for energy use periods typically ranging from years down to days. That is, given sufficient historical energy use data (“baseline data”), a model can be developed using these normalization techniques that can be used to accurately estimate the energy consumption of the building as a function of outside temperature. These estimates are used to remove weather effects from an energy use profile and also to predict energy use as a function of temperature.
The present inventors have observed, however, that conventional normalization techniques, utterly fail to be accurate and useful when energy use data granularity is less than a 24-hour period. Normalization models that are derived from energy use data having granularities on the order of six hours, one hour, 15 minutes, etc., have been shown to be exceedingly deficient in accuracy and are thus unreliable.
Accordingly, what is needed is a technique that provides for accurately estimating energy use as a function of temperature, where the technique is derived from and is applicable to, energy consumption periods less than 24 hours.
What is also needed is an apparatus and method for employing fine-grained (i.e., less than 24 hours) energy use data to derive an accurate model for energy use based upon outside temperature.
What is additionally needed is a fine-grained baseline energy data weather normalization apparatus and method.
What is further needed is a system for characterizing a building's energy consumption as a function of temperature that is applicable at resolutions less than one day.
What is moreover needed are mechanisms that understand and employ the transient energy use responses of buildings for purposes of energy consumption predictions covering individual buildings, groups of buildings, and larger areas.